Nonlinear Tire Model Approximation Using Machine Learning for Efficient Model Predictive Control

Model Predictive Controller (MPC) is widely used as a technique for path tracking control since it allows for dealing with system constraints and future forecasts. However, the performance of MPC is directly affected by the adopted model. A complex dynamic model can guarantee accuracy in path tracking but may not be suitable in computational terms. On the other hand, a simplified model may not capture essential nonlinear aspects. Thus, to cope with these problems, this paper deals with data-driven tire modeling to improve autonomous ground vehicle path tracking control. The main contribution of the present work is to show that neural tires can capture the nonlinearities present in the interaction between lateral and longitudinal vehicle dynamics, with a reduced computational cost for predictive controllers. Simulated and experimental tire data are approximate to design data-driven tire models using radial basis function and multilayer perceptron neural networks. Then, based on ground vehicles with neural tires, model predictive controllers are designed to regulate wheel torque and steering angle inputs. Comparative tests were conducted to compare the proposed data-driven MPC approach with the classical nonlinear MPC controller. The results show that the neural tires approximate nonlinear tire models and experimental data with arbitrary precision in terms of accuracy and error-based metrics. The proposed methodology was successfully applied to perform trajectory and velocity tracking of ground vehicles. In addition, MPC with a neural tire model as prediction inference reduces the computational effort compared to traditional approaches.